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Gaming on Linux on M1 is here! We’re thrilled to release our Asahi game playing toolkit, which integrates our Vulkan 1.3 drivers with x86 emulation and Windows compatibility. Plus a bonus: conformant OpenCL 3.0. Asahi Linux now ships the only conformant OpenGL®, OpenCL™, and Vulkan® drivers for this hardware. As for gaming… while today’s release is an alpha, Control runs well! Installation First, install Fedora Asahi Remix. Once installed, get the latest drivers with dnf upgrade --refresh && reboot. Then just dnf install steam and play. While all M1/M2-series systems work, most games require 16GB of memory due to emulation overhead. The stack Games are typically x86 Windows binaries rendering with DirectX, while our target is Arm Linux with Vulkan. We need to handle each difference: FEX emulates x86 on Arm. Wine translates Windows to Linux. DXVK and vkd3d-proton translate DirectX to Vulkan. There’s one curveball: page size. Operating systems allocate memory in fixed size “pages”. If an application expects smaller pages than the system uses, they will break due to insufficient alignment of allocations. That’s a problem: x86 expects 4K pages but Apple systems use 16K pages. While Linux can’t mix page sizes between processes, it can virtualize another Arm Linux kernel with a different page size. So we run games inside a tiny virtual machine using muvm, passing through devices like the GPU and game controllers. The hardware is happy because the system is 16K, the game is happy because the virtual machine is 4K, and you’re happy because you can play Fallout 4. Vulkan The final piece is an adult-level Vulkan driver, since translating DirectX requires Vulkan 1.3 with many extensions. Back in April, I wrote Honeykrisp, the only Vulkan 1.3 driver for Apple hardware. I’ve since added DXVK support. Let’s look at some new features. Tessellation Tessellation enables games like The Witcher 3 to generate geometry. The M1 has hardware tessellation, but it is too limited for DirectX, Vulkan, or OpenGL. We must instead tessellate with arcane compute shaders, as detailed in today’s talk at XDC2024. Geometry shaders Geometry shaders are an older, cruder method to generate geometry. Like tessellation, the M1 lacks geometry shader hardware so we emulate with compute. Is that fast? No, but geometry shaders are slow even on desktop GPUs. They don’t need to be fast – just fast enough for games like Ghostrunner. Enhanced robustness “Robustness” permits an application’s shaders to access buffers out-of-bounds without crashing the hardware. In OpenGL and Vulkan, out-of-bounds loads may return arbitrary elements, and out-of-bounds stores may corrupt the buffer. Our OpenGL driver exploits this definition for efficient robustness on the M1. Some games require stronger guarantees. In DirectX, out-of-bounds loads return zero, and out-of-bounds stores are ignored. DXVK therefore requires VK_EXT_robustness2, a Vulkan extension strengthening robustness. Like before, we implement robustness with compare-and-select instructions. A naïve implementation would compare a loaded index with the buffer size and select a zero result if out-of-bounds. However, our GPU loads are vector while arithmetic is scalar. Even if we disabled page faults, we would need up to four compare-and-selects per load. load R, buffer, index * 16 ulesel R[0], index, size, R[0], 0 ulesel R[1], index, size, R[1], 0 ulesel R[2], index, size, R[2], 0 ulesel R[3], index, size, R[3], 0 There’s a trick: reserve 64 gigabytes of zeroes using virtual memory voodoo. Since every 32-bit index multiplied by 16 fits in 64 gigabytes, any index into this region loads zeroes. For out-of-bounds loads, we simply replace the buffer address with the reserved address while preserving the index. Replacing a 64-bit address costs just two 32-bit compare-and-selects. ulesel buffer.lo, index, size, buffer.lo, RESERVED.lo ulesel buffer.hi, index, size, buffer.hi, RESERVED.hi load R, buffer, index * 16 Two instructions, not four. Next steps Sparse texturing is next for Honeykrisp, which will unlock more DX12 games. The alpha already runs DX12 games that don’t require sparse, like Cyberpunk 2077. While many games are playable, newer AAA titles don’t hit 60fps yet. Correctness comes first. Performance improves next. Indie games like Hollow Knight do run full speed. Beyond gaming, we’re adding general purpose x86 emulation based on this stack. For more information, see the FAQ. Today’s alpha is a taste of what’s to come. Not the final form, but enough to enjoy Portal 2 while we work towards “1.0”. Acknowledgements This work has been years in the making with major contributions from… Alyssa Rosenzweig Asahi Lina chaos_princess Davide Cavalca Dougall Johnson Ella Stanforth Faith Ekstrand Janne Grunau Karol Herbst marcan Mary Guillemard Neal Gompa Sergio López TellowKrinkle Teoh Han Hui Rob Clark Ryan Houdek … Plus hundreds of developers whose work we build upon, spanning the Linux, Mesa, Wine, and FEX projects. Today’s release is thanks to the magic of open source. We hope you enjoy the magic. Happy gaming.
u{text-decoration-thickness:0.09em;text-decoration-color:skyblue} Finally, conformant Vulkan for the M1! The new “Honeykrisp” driver is the first conformant Vulkan® for Apple hardware on any operating system, implementing the full 1.3 spec without “portability” waivers. Honeykrisp is not yet released for end users. We’re continuing to add features, improve performance, and port to more hardware. Source code is available for developers. HoloCure running on Honeykrisp ft. DXVK, FEX, and Proton. Honeykrisp is not based on prior M1 Vulkan efforts, but rather Faith Ekstrand’s open source NVK driver for NVIDIA GPUs. In her words: All Vulkan drivers in Mesa trace their lineage to the Intel Vulkan driver and started by copying+pasting from it. My hope is that NVK will eventually become the driver that everyone copies and pastes from. To that end, I’m building NVK with all the best practices we’ve developed for Vulkan drivers over the last 7.5 years and trying to keep the code-base clean and well-organized. Why spend years implementing features from scratch when we can reuse NVK? There will be friction starting out, given NVIDIA’s desktop architecture differs from the M1’s mobile roots. In exchange, we get a modern driver designed for desktop games. We’ll need to pass a half-million tests ensuring correctness, submit the results, and then we’ll become conformant after 30 days of industry review. Starting from NVK and our OpenGL 4.6 driver… can we write a driver passing the Vulkan 1.3 conformance test suite faster than the 30 day review period? It’s unprecedented… Challenge accepted. April 2 It begins with a text. Faith… I think I want to write a Vulkan driver. Her advice? Just start typing. Thre’s no copy-pasting yet – we just add M1 code to NVK and remove NVIDIA as we go. Since the kernel mediates our access to the hardware, we begin connecting “NVK” to Asahi Lina’s kernel driver using code shared with OpenGL. Then we plug in our shader compiler and hit the hay. April 3 To access resources, GPUs use “descriptors” containing the address, format, and size of a resource. Vulkan bundles descriptors into “sets” per the application’s “descriptor set layout”. When compiling shaders, the driver lowers descriptor accesses to marry the set layout with the hardware’s data structures. As our descriptors differ from NVIDIA’s, our next task is adapting NVK’s descriptor set lowering. We start with a simple but correct approach, deleting far more code than we add. April 4 With working descriptors, we can compile compute shaders. Now we program the fixed-function hardware to dispatch compute. We first add bookkeeping to map Vulkan command buffers to lists of M1 “control streams”, then we generate a compute control stream. We copy that code from our OpenGL driver, translate the GL into Vulkan, and compute works. That’s enough to move on to “copies” of buffers and images. We implement Vulkan’s copies with compute shaders, internally dispatched with Vulkan commands as if we were the application. The first copy test passes. April 5 Fleshing out yesterday’s code, all copy tests pass. April 6 We’re ready to tackle graphics. The novelty is handling graphics state like depth/stencil. That’s straightforward, but there’s a lot of state to handle. Faith’s code collects all “dynamic state” into a single structure, which we translate into hardware control words. As usual, we grab that translation from our OpenGL driver, blend with NVK, and move on. April 7 What makes state “dynamic”? Dynamic state can change without recompiling shaders. By contrast, static state is baked into shader binaries called “pipelines”. If games create all their pipelines during a loading screen, there is no compiler “stutter” during gameplay. The idea hasn’t quite panned out: many game developers don’t know their state ahead-of-time so cannot create pipelines early. In response, Vulkan has made ever more state dynamic, punctuated with the EXT_shader_object extension that makes pipelines optional. We want full dynamic state and shader objects. Unfortunately, the M1 bakes random state into shaders: vertex attributes, fragment outputs, blending, even linked interpolation qualifiers. Like most of the industry in the 2010s, the M1’s designers bet on pipelines. Faced with this hardware, a reasonable driver developer would double-down on pipelines. DXVK would stutter, but we’d pass conformance. I am not reasonable. To eliminate stuttering in OpenGL, we make state dynamic with four strategies: Conditional code. Precompiled variants. Indirection. Prologs and epilogs. Wait, what-a-logs? AMD also bakes state into shaders… with a twist. They divide the hardware binary into three parts: a prolog, the shader, and an epilog. Confining dynamic state to the periphery eliminates shader variants. They compile prologs and epilogs on the fly, but that’s fast and doesn’t stutter. Linking shader parts is a quick concatenation, or long jumps avoid linking altogether. This strategy works for the M1, too. For Honeykrisp, let’s follow NVK’s lead and treat all state as dynamic. No other Vulkan driver has implemented full dynamic state and shader objects this early on, but it avoids refactoring later. Today we add the code to build, compile, and cache prologs and epilogs. Putting it together, we get a (dynamic) triangle: April 8 Guided by the list of failing tests, we wire up the little bits missed along the way, like translating border colours. /* Translate an American VkBorderColor into a Canadian agx_border_colour */ enum agx_border_colour translate_border_color(VkBorderColor color) { switch (color) { case VK_BORDER_COLOR_INT_TRANSPARENT_BLACK: return AGX_BORDER_COLOUR_TRANSPARENT_BLACK; ... } } Test results are getting there. Pass: 149770, Fail: 7741, Crash: 2396 That’s good enough for vkQuake. April 9 Lots of little fixes bring us to a 99.6% pass rate… for Vulkan 1.1. Why stop there? NVK is 1.3 conformant, so let’s claim 1.3 and skip to the finish line. Pass: 255209, Fail: 3818, Crash: 599 98.3% pass rate for 1.3 on our 1 week anniversary. Not bad. April 10 SuperTuxKart has a Vulkan renderer. April 11 Zink works too. April 12 I tracked down some fails to a test bug, where an arbitrary verification threshold was too strict to pass on some devices. I filed a bug report, and it’s resolved within a few weeks. April 16 The tests for “descriptor indexing” revealed a compiler bug affecting subgroup shuffles in non-uniform control flow. The M1’s shuffle instruction is quirky, but it’s easy to workaround. Fixing that fixes the descriptor indexing tests. April 17 A few tests crash inside our register allocator. Their shaders contain a peculiar construction: if (condition) { while (true) { } } condition is always false, but the compiler doesn’t know that. Infinite loops are nominally invalid since shaders must terminate in finite time, but this shader is syntactically valid. “All loops contain a break” seems obvious for a shader, but it’s false. It’s straightforward to fix register allocation, but what a doozy. April 18 Remember copies? They’re slow, and every frame currently requires a copy to get on screen. For “zero copy” rendering, we need enough Linux window system integration to negotiate an efficient surface layout across process boundaries. Linux uses “modifiers” for this purpose, so we implement the EXT_image_drm_format_modifier extension. And by implement, I mean copy. Copies to avoid copies. April 20 “I’d like a 4K x86 Windows Direct3D PC game on a 16K arm64 Linux Vulkan Mac.” … “Ma’am, this is a Wendy’s.” April 22 As bug fixing slows down, we step back and check our driver architecture. Since we treat all state as dynamic, we don’t pre-pack control words during pipeline creation. That adds theoretical CPU overhead. Is that a problem? After some optimization, vkoverhead says we’re pushing 100 million draws per second. I think we’re okay. April 24 Time to light up YCbCr. If we don’t use special YCbCr hardware, this feature is “software-only”. However, it touches a lot of code. It touches so much code that Mohamed Ahmed spent an entire summer adding it to NVK. Which means he spent a summer adding it to Honeykrisp. Thanks, Mohamed ;-) April 25 Query copies are next. In Vulkan, the application can query the number of samples rendered, writing the result into an opaque “query pool”. The result can be copied from the query pool on the CPU or GPU. For the CPU, the driver maps the pool’s internal data structure and copies the result. This may require nontrivial repacking. For the GPU, we need to repack in a compute shader. That’s harder, because we can’t just run C code on the GPU, right? …Actually, we can. A little witchcraft makes GPU query copies as easy as C. void copy_query(struct params *p, int i) { uintptr_t dst = p->dest + i * p->stride; int query = p->first + i; if (p->available[query] || p->partial) { int q = p->index[query]; write_result(dst, p->_64, p->results[q]); } ... } April 26 The final boss: border colours, hard mode. Direct3D lets the application choose an arbitrary border colour when creating a sampler. By contrast, Vulkan only requires three border colours: (0, 0, 0, 0) – transparent black (0, 0, 0, 1) – opaque black (1, 1, 1, 1) – opaque white We handled these on April 8. Unfortunately, there are two problems. First, we need custom border colours for Direct3D compatibility. Both DXVK and vkd3d-proton require the EXT_custom_border_color extension. Second, there’s a subtle problem with our hardware, causing dozens of fails even without custom border colours. To understand the issue, let’s revisit texture descriptors, which contain a pixel format and a component reordering swizzle. Some formats are implicitly reordered. Common “BGRA” formats swap red and blue for historical reasons. The M1 does not directly support these formats. Instead, the driver composes the swizzle with the format’s reordering. If the application uses a BARB swizzle with a BGRA format, the driver uses an RABR swizzle with an RGBA format. There’s a catch: swizzles apply to the border colour, but formats do not. We need to undo the format reordering when programming the border colour for correct results after the hardware applies the composed swizzle. Our OpenGL driver implements border colours this way, because it knows the texture format when creating the sampler. Unfortunately, Vulkan doesn’t give us that information. Without custom border colour support, we “should” be okay. Swapping red and blue doesn’t change anything if the colour is white or black. There’s an even subtler catch. Vulkan mandates support for a packed 16-bit format with 4-bit components. The M1 supports a similar format… but with reversed “endianness”, swapping red and alpha. That still seems okay. For transparent black (all zero) and opaque white (all one), swapping components doesn’t change the result. The problem is opaque black: (0, 0, 0, 1). Swapping red and alpha gives (1, 0, 0, 0). Transparent red? Uh-oh. We’re stuck. No known hardware configuration implements correct Vulkan semantics. Is hope lost? Do we give up? A reasonable person would. I am not reasonable. Let’s jump into the deep end. If we implement custom border colours, opaque black becomes a special case. But how? The M1’s custom border colours entangle the texture format with the sampler. A reasonable person would skip Direct3D support. As you know, I am not reasonable. Although the hardware is unsuitable, we control software. Whenever a shader samples a texture, we’ll inject code to fix up the border colour. This emulation is simple, correct, and slow. We’ll use dirty driver tricks to speed it up later. For now, we eat the cost, advertise full custom border colours, and pass the opaque black tests. April 27 All that’s left is some last minute bug fixing, and… Pass: 686930, Fail: 0 Success. The future The next task is implementing everything that DXVK and vkd3d-proton require to layer Direct3D. That includes esoteric extensions like transform feedback. Then Wine and an open source x86 emulator will run Windows games on Asahi Linux. That’s getting ahead of ourselves. In the mean time, enjoy Linux games with our conformant OpenGL 4.6 drivers… and stay tuned. Baby Storm running on Honeykrisp ft. DXVK, FEX, and Proton.
For years, the M1 has only supported OpenGL 4.1. That changes today – with our release of full OpenGL® 4.6 and OpenGL® ES 3.2! Install Fedora for the latest M1/M2-series drivers. Already installed? Just dnf –refresh upgrade. Unlike the vendor’s non-conformant 4.1 drivers, our open source Linux drivers are conformant to the latest OpenGL versions, finally promising broad compatibility with modern OpenGL workloads, like Blender, Ryujinx, and Citra. Conformant 4.6/3.2 drivers must pass over 100,000 tests to ensure correctness. The official list of conformant drivers now includes our OpenGL 4.6 and ES 3.2. While the vendor doesn’t yet support graphics standards like modern OpenGL, we do. For this Valentine’s Day, we want to profess our love for interoperable open standards. We want to free users and developers from lock-in, enabling applications to run anywhere the heart wants without special ports. For that, we need standards conformance. Six months ago, we became the first conformant driver for any standard graphics API for the M1 with the release of OpenGL ES 3.1 drivers. Today, we’ve finished OpenGL with the full 4.6… and we’re well on the road to Vulkan. Compared to 4.1, OpenGL 4.6 adds dozens of required features, including: Robustness SPIR-V Clip control Cull distance Compute shaders Upgraded transform feedback Regrettably, the M1 doesn’t map well to any graphics standard newer than OpenGL ES 3.1. While Vulkan makes some of these features optional, the missing features are required to layer DirectX and OpenGL on top. No existing solution on M1 gets past the OpenGL 4.1 feature set. How do we break the 4.1 barrier? Without hardware support, new features need new tricks. Geometry shaders, tessellation, and transform feedback become compute shaders. Cull distance becomes a transformed interpolated value. Clip control becomes a vertex shader epilogue. The list goes on. For a taste of the challenges we overcame, let’s look at robustness. Built for gaming, GPUs traditionally prioritize raw performance over safety. Invalid application code, like a shader that reads a buffer out-of-bounds, can trigger undefined behaviour. Drivers exploit that to maximize performance. For applications like web browsers, that trade-off is undesirable. Browsers handle untrusted shaders, which they must sanitize to ensure stability and security. Clicking a malicious link should not crash the browser. While some sanitization is necessary as graphics APIs are not security barriers, reducing undefined behaviour in the API can assist “defence in depth”. “Robustness” features can help. Without robustness, out-of-bounds buffer access in a shader can crash. With robustness, the application can opt for defined out-of-bounds behaviour, trading some performance for less attack surface. All modern cross-vendor APIs include robustness. Many games even (accidentally?) rely on robustness. Strangely, the vendor’s proprietary API omits buffer robustness. We must do better for conformance, correctness, and compatibility. Let’s first define the problem. Different APIs have different definitions of what an out-of-bounds load returns when robustness is enabled: Zero (Direct3D, Vulkan with robustBufferAccess2) Either zero or some data in the buffer (OpenGL, Vulkan with robustBufferAccess) Arbitrary values, but can’t crash (OpenGL ES) OpenGL uses the second definition: return zero or data from the buffer. One approach is to return the last element of the buffer for out-of-bounds access. Given the buffer size, we can calculate the last index. Now consider the minimum of the index being accessed and the last index. That equals the index being accessed if it is valid, and some other valid index otherwise. Loading the minimum index is safe and gives a spec-compliant result. As an example, a uniform buffer load without robustness might look like: load.i32 result, buffer, index Robustness adds a single unsigned minimum (umin) instruction: umin idx, index, last load.i32 result, buffer, idx Is the robust version slower? It can be. The difference should be small percentage-wise, as arithmetic is faster than memory. With thousands of threads running in parallel, the arithmetic cost may even be hidden by the load’s latency. There’s another trick that speeds up robust uniform buffers. Like other GPUs, the M1 supports “preambles”. The idea is simple: instead of calculating the same value in every thread, it’s faster to calculate once and reuse the result. The compiler identifies eligible calculations and moves them to a preamble executed before the main shader. These redundancies are common, so preambles provide a nice speed-up. We usually move uniform buffer loads to the preamble when every thread loads the same index. Since the size of a uniform buffer is fixed, extra robustness arithmetic is also moved to the preamble. The robustness is “free” for the main shader. For robust storage buffers, the clamping might move to the preamble even if the load or store cannot. Armed with robust uniform and storage buffers, let’s consider robust “vertex buffers”. In graphics APIs, the application can set vertex buffers with a base GPU address and a chosen layout of “attributes” within each buffer. Each attribute has an offset and a format, and the buffer has a “stride” indicating the number of bytes per vertex. The vertex shader can then read attributes, implicitly indexing by the vertex. To do so, the shader loads the address: Some hardware implements robust vertex fetch natively. Other hardware has bounds-checked buffers to accelerate robust software vertex fetch. Unfortunately, the M1 has neither. We need to implement vertex fetch with raw memory loads. One instruction set feature helps. In addition to a 64-bit base address, the M1 GPU’s memory loads also take an offset in elements. The hardware shifts the offset and adds to the 64-bit base to determine the address to fetch. Additionally, the M1 has a combined integer multiply-add instruction imad. Together, these features let us implement vertex loads in two instructions. For example, a 32-bit attribute load looks like: imad idx, stride/4, vertex, offset/4 load.i32 result, base, idx The hardware load can perform an additional small shift. Suppose our attribute is a vector of 4 32-bit values, densely packed into a buffer with no offset. We can load that attribute in one instruction: load.v4i32 result, base, vertex << 2 …with the hardware calculating the address: What about robustness? We want to implement robustness with a clamp, like we did for uniform buffers. The problem is that the vertex buffer size is given in bytes, while our optimized load takes an index in “vertices”. A single vertex buffer can contain multiple attributes with different formats and offsets, so we can’t convert the size in bytes to a size in “vertices”. Let’s handle the latter problem. We can rewrite the addressing equation as: That is: one buffer with many attributes at different offsets is equivalent to many buffers with one attribute and no offset. This gives an alternate perspective on the same data layout. Is this an improvement? It avoids an addition in the shader, at the cost of passing more data – addresses are 64-bit while attribute offsets are 16-bit. More importantly, it lets us translate the vertex buffer size in bytes into a size in “vertices” for each vertex attribute. Instead of clamping the offset, we clamp the vertex index. We still make full use of the hardware addressing modes, now with robustness: umin idx, vertex, last valid load.v4i32 result, base, idx << 2 We need to calculate the last valid vertex index ahead-of-time for each attribute. Each attribute has a format with a particular size. Manipulating the addressing equation, we can calculate the last byte accessed in the buffer (plus 1) relative to the base: The load is valid when that value is bounded by the buffer size in bytes. We solve the integer inequality as: The driver calculates the right-hand side and passes it into the shader. One last problem: what if a buffer is too small to load anything? Clamping won’t save us – the code would clamp to a negative index. In that case, the attribute is entirely invalid, so we swap the application’s buffer for a small buffer of zeroes. Since we gave each attribute its own base address, this determination is per-attribute. Then clamping the index to zero correctly loads zeroes. Putting it together, a little driver math gives us robust buffers at the cost of one umin instruction. In addition to buffer robustness, we need image robustness. Like its buffer counterpart, image robustness requires that out-of-bounds image loads return zero. That formalizes a guarantee that reasonable hardware already makes. …But it would be no fun if our hardware was reasonable. Running the conformance tests for image robustness, there is a single test failure affecting “mipmapping”. For background, mipmapped images contain multiple “levels of detail”. The base level is the original image; each successive level is the previous level downscaled. When rendering, the hardware selects the level closest to matching the on-screen size, improving efficiency and visual quality. With robustness, the specifications all agree that image loads return… Zero if the X- or Y-coordinate is out-of-bounds Zero if the level is out-of-bounds Meanwhile, image loads on the M1 GPU return… Zero if the X- or Y-coordinate is out-of-bounds Values from the last level if the level is out-of-bounds Uh-oh. Rather than returning zero for out-of-bounds levels, the hardware clamps the level and returns nonzero values. It’s a mystery why. The vendor does not document their hardware publicly, forcing us to rely on reverse engineering to build drivers. Without documentation, we don’t know if this behaviour is intentional or a hardware bug. Either way, we need a workaround to pass conformance. The obvious workaround is to never load from an invalid level: if (level <= levels) { return imageLoad(x, y, level); } else { return 0; } That involves branching, which is inefficient. Loading an out-of-bounds level doesn’t crash, so we can speculatively load and then use a compare-and-select operation instead of branching: vec4 data = imageLoad(x, y, level); return (level <= levels) ? data : 0; This workaround is okay, but it could be improved. While the M1 GPU has combined compare-and-select instructions, the instruction set is scalar. Each thread processes one value at a time, not a vector of multiple values. However, image loads return a vector of four components (red, green, blue, alpha). While the pseudo-code looks efficient, the resulting assembly is not: image_load R, x, y, level ulesel R[0], level, levels, R[0], 0 ulesel R[1], level, levels, R[1], 0 ulesel R[2], level, levels, R[2], 0 ulesel R[3], level, levels, R[3], 0 Fortunately, the vendor driver has a trick. We know the hardware returns zero if either X or Y is out-of-bounds, so we can force a zero output by setting X or Y out-of-bounds. As the maximum image size is 16384 pixels wide, any X greater than 16384 is out-of-bounds. That justifies an alternate workaround: bool valid = (level <= levels); int x_ = valid ? x : 20000; return imageLoad(x_, y, level); Why is this better? We only change a single scalar, not a whole vector, compiling to compact scalar assembly: ulesel x_, level, levels, x, #20000 image_load R, x_, y, level If we preload the constant to a uniform register, the workaround is a single instruction. That’s optimal – and it passes conformance. Blender “Wanderer” demo by Daniel Bystedt, licensed CC BY-SA.
Conformant OpenGL® ES 3.1 drivers are now available for M1- and M2-family GPUs. That means the drivers are compatible with any OpenGL ES 3.1 application. Interested? Just install Linux! For existing Asahi Linux users, upgrade your system with dnf upgrade (Fedora) or pacman -Syu (Arch) for the latest drivers. Our reverse-engineered, free and open source graphics drivers are the world’s only conformant OpenGL ES 3.1 implementation for M1- and M2-family graphics hardware. That means our driver passed tens of thousands of tests to demonstrate correctness and is now recognized by the industry. To become conformant, an “implementation” must pass the official conformance test suite, designed to verify every feature in the specification. The test results are submitted to Khronos, the standards body. After a 30-day review period, if no issues are found, the implementation becomes conformant. The Khronos website lists all conformant implementations, including our drivers for the M1, M1 Pro/Max/Ultra, M2, and M2 Pro/Max. Today’s milestone isn’t just about OpenGL ES. We’re releasing the first conformant implementation of any graphics standard for the M1. And we don’t plan to stop here ;-) Unlike ours, the manufacturer’s M1 drivers are unfortunately not conformant for any standard graphics API, whether Vulkan or OpenGL or OpenGL ES. That means that there is no guarantee that applications using the standards will work on your M1/M2 (if you’re not running Linux). This isn’t just a theoretical issue. Consider Vulkan. The third-party MoltenVK layers a subset of Vulkan on top of the proprietary drivers. However, those drivers lack key functionality, breaking valid Vulkan applications. That hinders developers and users alike, if they haven’t yet switched their M1/M2 computers to Linux. Why did we pursue standards conformance when the manufacturer did not? Above all, our commitment to quality. We want our users to know that they can depend on our Linux drivers. We want standard software to run without M1-specific hacks or porting. We want to set the right example for the ecosystem: the way forward is implementing open standards, conformant to the specifications, without compromises for “portability”. We are not satisfied with proprietary drivers, proprietary APIs, and refusal to implement standards. The rest of the industry knows that progress comes from cross-vendor collaboration. We know it, too. Achieving conformance is a win for our community, for open source, and for open graphics. Of course, Asahi Lina and I are two individuals with minimal funding. It’s a little awkward that we beat the big corporation… It’s not too late though. They should follow our lead! OpenGL ES 3.1 updates the experimental OpenGL ES 3.0 and OpenGL 3.1 we shipped in June. Notably, ES 3.1 adds compute shaders, typically used to accelerate general computations within graphics applications. For example, a 3D game could run its physics simulations in a compute shader. The simulation results can then be used for rendering, eliminating stalls that would otherwise be required to synchronize the GPU with a CPU physics simulation. That lets the game run faster. Let’s zoom in on one new feature: atomics on images. Older versions of OpenGL ES allowed an application to read an image in order to display it on screen. ES 3.1 allows the application to write to the image, typically from a compute shader. This new feature enables flexible image processing algorithms, which previously needed to fit into the fixed-function 3D pipeline. However, GPUs are massively parallel, running thousands of threads at the same time. If two threads write to the same location, there is a conflict: depending which thread runs first, the result will be different. We have a race condition. “Atomic” access to memory provides a solution to race conditions. With atomics, special hardware in the memory subsystem guarantees consistent, well-defined results for select operations, regardless of the order of the threads. Modern graphics hardware supports various atomic operations, like addition, serving as building blocks to complex parallel algorithms. Can we put these two features together to write to an image atomically? Yes. A ubiquitous OpenGL ES extension, required for ES 3.2, adds atomics operating on pixels in an image. For example, a compute shader could atomically increment the value at pixel (10, 20). Other GPUs have dedicated instructions to perform atomics on an images, making the driver implementation straightforward. For us, the story is more complicated. The M1 lacks hardware instructions for image atomics, even though it has non-image atomics and non-atomic images. We need to reframe the problem. The idea is simple: to perform an atomic on a pixel, we instead calculate the address of the pixel in memory and perform a regular atomic on that address. Since the hardware supports regular atomics, our task is “just” calculating the pixel’s address. If the image were laid out linearly in memory, this would be straightforward: multiply the Y-coordinate by the number of bytes per row (“stride”), multiply the X-coordinate by the number of bytes per pixel, and add. That gives the pixel’s offset in bytes relative to the first pixel of the image. To get the final address, we add that offset to the address of the first pixel. Alas, images are rarely linear in memory. To improve cache efficiency, modern graphics hardware interleaves the X- and Y-coordinates. Instead of one row after the next, pixels in memory follow a spiral-like curve. We need to amend our previous equation to interleave the coordinates. We could use many instructions to mask one bit at a time, shifting to construct the interleaved result, but that’s inefficient. We can do better. There is a well-known “bit twiddling” algorithm to interleave bits. Rather than shuffle one bit at a time, the algorithm shuffles groups of bits, parallelizing the problem. Implementing this algorithm in shader code improves performance. In practice, only the lower 7-bits (or less) of each coordinate are interleaved. That lets us use 32-bit instructions to “vectorize” the interleave, by putting the X- and Y-coordinates in the low and high 16-bits of a 32-bit register. Those 32-bit instructions let us interleave X and Y at the same time, halving the instruction count. Plus, we can exploit the GPU’s combined shift-and-add instruction. Putting the tricks together, we interleave in 10 instructions of M1 GPU assembly: # Inputs x, y in r0l, r0h. # Output in r1. add r2, #0, r0, lsl 4 or r1, r0, r2 and r1, r1, #0xf0f0f0f add r2, #0, r1, lsl 2 or r1, r1, r2 and r1, r1, #0x33333333 add r2, #0, r1, lsl 1 or r1, r1, r2 and r1, r1, #0x55555555 add r1, r1l, r1h, lsl 1 We could stop here, but what if there’s a dedicated instruction to interleave bits? PowerVR has a “shuffle” instruction shfl, and the M1 GPU borrows from PowerVR. Perhaps that instruction was borrowed too. Unfortunately, even if it was, the proprietary compiler won’t use it when compiling our test shaders. That makes it difficult to reverse-engineer the instruction – if it exists – by observing compiled shaders. It’s time to dust off a powerful reverse-engineering technique from magic kindergarten: guess and check. Dougall Johnson provided the guess. When considering the instructions we already know about, he took special notice of the “reverse bits” instruction. Since reversing bits is a type of bit shuffle, the interleave instruction should be encoded similarly. The bit reverse instruction has a two-bit field specifying the operation, with value 01. Related instructions to count the number of set bits and find the first set bit have values 10 and 11 respectively. That encompasses all known “complex bit manipulation” instructions. tr:first-child > td:nth-child(2) { text-align:center !important } td > strong > a:visited { color: #0000EE } 00 ? ? ? 01 Reverse bits 10 Count set bits 11 Find first set There is one value of the two-bit enumeration that is unobserved and unknown: 00. If this interleave instruction exists, it’s probably encoded like the bit reverse but with operation code 00 instead of 01. There’s a difficulty: the three known instructions have one single input source, but our instruction interleaves two sources. Where does the second source go? We can make a guess based on symmetry. Presumably to simplify the hardware decoder, M1 GPU instructions usually encode their sources in consistent locations across instructions. The other three instructions have a gap where we would expect the second source to be, in a two-source arithmetic instruction. Probably the second source is there. Armed with a guess, it’s our turn to check. Rather than handwrite GPU assembly, we can hack our compiler to replace some two-source integer operation (like multiply) with our guessed encoding of “interleave”. Then we write a compute shader using this operation (by “multiplying” numbers) and run it with the newfangled compute support in our driver. All that’s left is writing a shader that checks that the mystery instruction returns the interleaved result for each possible input. Since the instruction takes two 16-bit sources, there are about 4 billion (\(2^32\)) inputs. With our driver, the M1 GPU manages to check them all in under a second, and the verdict is in: this is our interleave instruction. As for our clever vectorized assembly to interleave coordinates? We can replace it with one instruction. It’s anticlimactic, but it’s fast and it passes the conformance tests. And that’s what matters. Thank you to Khronos and Software in the Public Interest for supporting open drivers.
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I've been running the Framework Desktop for a few months here in Copenhagen now. It's an incredible machine. It's completely quiet, even under heavy, stress-all-cores load. It's tiny too, at just 4.5L of volume, especially compared to my old beautiful but bulky North tower running the 7950X — yet it's faster! And finally, it's simply funky, quirky, and fun! In some ways, the Framework Desktop is a curious machine. Desktop PCs are already very user-repairable! So why is Framework even bringing their talents to this domain? In the laptop realm, they're basically alone with that concept, but in the desktop space, it's rather crowded already. Yet it somehow still makes sense. Partly because Framework has gone with the AMD Ryzen AI Max 395+, which is technically a laptop CPU. You can find it in the ASUS ROG Flow Z13 and the HP ZBook Ultra. Which means it'll fit in a tiny footprint, and Framework apparently just wanted to see what they could do in that form factor. They clearly had fun with it. Look at mine: There are 21 little tiles on the front that you can get in a bunch of different colors or with logos from Framework. Or you can 3D print your own! It's a welcome change in aesthetic from the brushed aluminum or gamer-focused RGBs approach that most of the competition is taking. But let's cut to the benchmarks. That's really why you'd buy a machine like the Framework Desktop. There are significantly cheaper mini PCs available from Beelink and others, but so far, Framework has the only AMD 395+ unit on sale that's completely silent (the GMKTec very much is not, nor is the Z3 Flow). And for me, that's just a dealbreaker. I can't listen to roaring fans anymore. Here's the key benchmark for me: That's the only type of multi-core workload I really sit around waiting on these days, and the Framework Desktop absolutely crushes it. It's almost twice as fast as the Beelink SER8 and still a solid third faster than the Beelink SER9 too. Of course, it's also a lot more expensive, but you're clearly getting some multi-core bang for your buck here! It's even a more dramatic difference to the Macs. It's a solid 40% faster than the M4 Max and 50% faster than the M4 Pro! Now some will say "that's just because Docker is faster on Linux," and they're not entirely wrong. Docker runs natively on Linux, so for this test, where the MySQL/Redis/ElasticSearch data stores run in Docker while Ruby and the app code runs natively, that's part of the answer. Last I checked, it was about 25% of the difference. But so what? Docker is an integral part of the workflow for tons of developers. We use it to be able to run different versions of MySQL, Redis, and ElasticSearch for different applications on the same machine at the same time. You can't really do that without Docker. So this is what Real World benchmarks reveal. It's not just about having a Docker advantage, though. The AMD 395+ is also incredibly potent in RAW CPU performance. Those 16 Zen5 cores are running at 5.1GHz, and in Geekbench 6 multicore, this is how they stack up: Basically matching the M4 Max! And a good chunk faster than the M4 Pro (as well as other AMDs and Intel's 14900K!). No wonder that it's crazy quick with a full-core stress test like running 30,000 assertions for our HEY test suite. To be fair, the M4s are faster in single-core performance. Apple holds the crown there. It's about 20%. And you'll see that in benchmarks like Speedometer, which mostly measures JavaScript single-core performance. The Framework Desktop puts out 670 vs 744 on the M4 Pro on Speedometer 2.1. On SP 3.1, it's an even bigger difference with 35 vs 50. But I've found that all these computers feel fast enough in single-core performance these days. I can't actually feel the difference browsing on a machine that does 670 vs 744 on SP2.1. Hell, I can barely feel the difference between the SER8, which does 506, and the M4 Pro! The only time I actually feel like I'm waiting on anything is in multi-core workloads like the HEY test suite, and here the AMD 395+ is very near the fastest you can get for a consumer desktop machine today at any price. It gets even better when you bring price into the equation, though. The Framework Desktop with 64GB RAM + 2TB NVMe is $1,876. To get a Mac Studio with similar specs — M4 Max, 64GB RAM, 2TB NVMe — you'll literally spend nearly twice as much at $3,299! If you go for 128GB RAM, you'll spend $2,276 on the Framework, but $4,099 on the Mac. And it'll still be way slower for development work using Docker! The Framework Desktop is simply a great deal. Speaking of 64GB vs 128GB, I've been running the 64GB version, and I almost never get anywhere close to the limits. I think the highest I've seen in regular use is about 20GB of RAM in action. Linux is really efficient. Especially when you're using a window manager like Hyprland, as we do in Omarchy. The only reason you really want to go for the full 128GB RAM is to run local LLM models. The AMD 395+ uses unified memory, like Apple, so nearly all of it is addressable to be used by the GPU. That means you can run monster models, like the new 120b gpt-oss from OpenAI. Framework has a video showing them pushing out 40 tokens/second doing just that. That seems about in range of the numbers I've seen from the M4 Max, which also seem in the 40-50 token/second range, but I'll defer to folks who benchmark local LLMs for the exact details on that. I tried running the new gpt-oss-20b on my 64GB machine, though, and I wasn't exactly blown away by the accuracy. In fact, I'd say it was pretty bad. I mean, exceptionally cool that it's doable, but very far off the frontier models we have access to as SaaS. So personally, this isn't yet something I actually use all that much in day-to-day development. I want the best models running at full speed, and right now that means SaaS. So if you just want the best, small computer that runs Linux superbly well out of the box, you should buy the Framework Desktop. It's completely quiet, fantastically fast, and super fun to look at. But I think it's also fair to mention that you can get something like a Beelink SER9 for half the price! Yes, it's also only 2/3 the performance in multi-core, but it's just as fast in single-core. Most developers could totally get away with the SER9, and barely notice what they were missing. But there are just as many people for whom the extra $1,000 is worth the price to run the test suite 40 seconds quicker! You know who you are. Oh, before I close, I also need to mention that this thing is a gaming powerhouse. It basically punches about as hard as an RTX 4060! With an iGPU! That's kinda crazy. Totally new territory on the PC side for integrated graphics. ETA Prime has a video showing the same chip in the GMK Tech running premier games at 1440p High Settings at great frame rates. You can run most games under Linux these days too (thanks Valve and Steam Deck!), but if you need to dual boot with Windows, the dual NVMe slots in the Framework Desktop come very handy. Framework did good with this one. AMD really blew it out of the water with the 395+. We're spoiled to have such incredible hardware available for Linux at such appealing discounts over similar stuff from Cupertino. What a great time to love open source software and tinker-friendly hardware!
I was listening to a podcast interview with the Jackson Browne (American singer/songwriter, political activist, and inductee into the Rock and Roll Hall of Fame) and the interviewer asks him how he approaches writing songs with social commentaries and critiques — something along the lines of: “How do you get from the New York Times headline on a social subject to the emotional heart of a song that matters to each individual?” Browne discusses how if you’re too subtle, people won’t know what you’re talking about. And if you’re too direct, you run the risk of making people feel like they’re being scolded. Here’s what he says about his songwriting: I want this to sound like you and I were drinking in a bar and we’re just talking about what’s going on in the world. Not as if you’re at some elevated place and lecturing people about something they should know about but don’t but [you think] they should care. You have to get to people where [they are, where] they do care and where they do know. I think that’s a great insight for anyone looking to have a connecting, effective voice. I know for me, it’s really easily to slide into a lecturing voice — you “should” do this and you “shouldn’t” do that. But I like Browne’s framing of trying to have an informal, conversational tone that meets people where they are. Like you’re discussing an issue in the bar, rather than listening to a sermon. Chris Coyier is the canonical example of this that comes to mind. I still think of this post from CSS Tricks where Chris talks about how to have submit buttons that go to different URLs: When you submit that form, it’s going to go to the URL /submit. Say you need another submit button that submits to a different URL. It doesn’t matter why. There is always a reason for things. The web is a big place and all that. He doesn’t conjure up some universally-applicable, justified rationale for why he’s sharing this method. Nor is there any pontificating on why this is “good” or “bad”. Instead, like most of Chris’ stuff, I read it as a humble acknowledgement of the practicalities at hand — “Hey, the world is a big place. People have to do crafty things to make their stuff work. And if you’re in that situation, here’s something that might help what ails ya.” I want to work on developing that kind of a voice because I love reading voices like that. Email · Mastodon · Bluesky
Previously, I wrote some sketchy ideas for what I call a p-fast trie, which is basically a wide fan-out variant of an x-fast trie. It allows you to find the longest matching prefix or nearest predecessor or successor of a query string in a set of names in O(log k) time, where k is the key length. My initial sketch was more complicated and greedy for space than necessary, so here’s a simplified revision. (“p” now stands for prefix.) layout A p-fast trie stores a lexicographically ordered set of names. A name is a sequence of characters from some small-ish character set. For example, DNS names can be represented as a set of about 50 letters, digits, punctuation and escape characters, usually one per byte of name. Names that are arbitrary bit strings can be split into chunks of 6 bits to make a set of 64 characters. Every unique prefix of every name is added to a hash table. An entry in the hash table contains: A shared reference to the closest name lexicographically greater than or equal to the prefix. Multiple hash table entries will refer to the same name. A reference to a name might instead be a reference to a leaf object containing the name. The length of the prefix. To save space, each prefix is not stored separately, but implied by the combination of the closest name and prefix length. A bitmap with one bit per possible character, corresponding to the next character after this prefix. For every other prefix that matches this prefix and is one character longer than this prefix, a bit is set in the bitmap corresponding to the last character of the longer prefix. search The basic algorithm is a longest-prefix match. Look up the query string in the hash table. If there’s a match, great, done. Otherwise proceed by binary chop on the length of the query string. If the prefix isn’t in the hash table, reduce the prefix length and search again. (If the empty prefix isn’t in the hash table then there are no names to find.) If the prefix is in the hash table, check the next character of the query string in the bitmap. If its bit is set, increase the prefix length and search again. Otherwise, this prefix is the answer. predecessor Instead of putting leaf objects in a linked list, we can use a more complicated search algorithm to find names lexicographically closest to the query string. It’s tricky because a longest-prefix match can land in the wrong branch of the implicit trie. Here’s an outline of a predecessor search; successor requires more thought. During the binary chop, when we find a prefix in the hash table, compare the complete query string against the complete name that the hash table entry refers to (the closest name greater than or equal to the common prefix). If the name is greater than the query string we’re in the wrong branch of the trie, so reduce the length of the prefix and search again. Otherwise search the set bits in the bitmap for one corresponding to the greatest character less than the query string’s next character; if there is one remember it and the prefix length. This will be the top of the sub-trie containing the predecessor, unless we find a longer match. If the next character’s bit is set in the bitmap, continue searching with a longer prefix, else stop. When the binary chop has finished, we need to walk down the predecessor sub-trie to find its greatest leaf. This must be done one character at a time – there’s no shortcut. thoughts In my previous note I wondered how the number of search steps in a p-fast trie compares to a qp-trie. I have some old numbers measuring the average depth of binary, 4-bit, 5-bit, 6-bit and 4-bit, 5-bit, dns qp-trie variants. A DNS-trie varies between 7 and 15 deep on average, depending on the data set. The number of steps for a search matches the depth for exact-match lookups, and is up to twice the depth for predecessor searches. A p-fast trie is at most 9 hash table probes for DNS names, and unlikely to be more than 7. I didn’t record the average length of names in my benchmark data sets, but I guess they would be 8–32 characters, meaning 3–5 probes. Which is far fewer than a qp-trie, though I suspect a hash table probe takes more time than chasing a qp-trie pointer. (But this kind of guesstimate is notoriously likely to be wrong!) However, a predecessor search might need 30 probes to walk down the p-fast trie, which I think suggests a linked list of leaf objects is a better option.
New Logic for Programmers Release! v0.11 is now available! This is over 20% longer than v0.10, with a new chapter on code proofs, three chapter overhauls, and more! Full release notes here. Software books I wish I could read I'm writing Logic for Programmers because it's a book I wanted to have ten years ago. I had to learn everything in it the hard way, which is why I'm ensuring that everybody else can learn it the easy way. Books occupy a sort of weird niche in software. We're great at sharing information via blogs and git repos and entire websites. These have many benefits over books: they're free, they're easily accessible, they can be updated quickly, they can even be interactive. But no blog post has influenced me as profoundly as Data and Reality or Making Software. There is no blog or talk about debugging as good as the Debugging book. It might not be anything deeper than "people spend more time per word on writing books than blog posts". I dunno. So here are some other books I wish I could read. I don't think any of them exist yet but it's a big world out there. Also while they're probably best as books, a website or a series of blog posts would be ok too. Everything about Configurations The whole topic of how we configure software, whether by CLI flags, environmental vars, or JSON/YAML/XML/Dhall files. What causes the configuration complexity clock? How do we distinguish between basic, advanced, and developer-only configuration options? When should we disallow configuration? How do we test all possible configurations for correctness? Why do so many widespread outages trace back to misconfiguration, and how do we prevent them? I also want the same for plugin systems. Manifests, permissions, common APIs and architectures, etc. Configuration management is more universal, though, since everybody either uses software with configuration or has made software with configuration. The Big Book of Complicated Data Schemas I guess this would kind of be like Schema.org, except with a lot more on the "why" and not the what. Why is important for the Volcano model to have a "smokingAllowed" field?1 I'd see this less as "here's your guide to putting Volcanos in your database" and more "here's recurring motifs in modeling interesting domains", to help a person see sources of complexity in their own domain. Does something crop up if the references can form a cycle? If a relationship needs to be strictly temporary, or a reference can change type? Bonus: path dependence in data models, where an additional requirement leads to a vastly different ideal data model that a company couldn't do because they made the old model. (This has got to exist, right? Business modeling is a big enough domain that this must exist. Maybe The Essence of Software touches on this? Man I feel bad I haven't read that yet.) Computer Science for Software Engineers Yes, I checked, this book does not exist (though maybe this is the same thing). I don't have any formal software education; everything I know was either self-taught or learned on the job. But it's way easier to learn software engineering that way than computer science. And I bet there's a lot of other engineers in the same boat. This book wouldn't have to be comprehensive or instructive: just enough about each topic to understand why it's an area of study and appreciate how research in it eventually finds its way into practice. MISU Patterns MISU, or "Make Illegal States Unrepresentable", is the idea of designing system invariants in the structure of your data. For example, if a Contact needs at least one of email or phone to be non-null, make it a sum type over EmailContact, PhoneContact, EmailPhoneContact (from this post). MISU is great. Most MISU in the wild look very different than that, though, because the concept of MISU is so broad there's lots of different ways to achieve it. And that means there are "patterns": smart constructors, product types, properly using sets, newtypes to some degree, etc. Some of them are specific to typed FP, while others can be used in even untyped languages. Someone oughta make a pattern book. My one request would be to not give them cutesy names. Do something like the Aarne–Thompson–Uther Index, where items are given names like "Recognition by manner of throwing cakes of different weights into faces of old uncles". Names can come later. The Tools of '25 Not something I'd read, but something to recommend to junior engineers. Starting out it's easy to think the only bit that matters is the language or framework and not realize the enormous amount of surrounding tooling you'll have to learn. This book would cover the basics of tools that enough developers will probably use at some point: git, VSCode, very basic Unix and bash, curl. Maybe the general concepts of tools that appear in every ecosystem, like package managers, build tools, task runners. That might be easier if we specialize this to one particular domain, like webdev or data science. Ideally the book would only have to be updated every five years or so. No LLM stuff because I don't expect the tooling will be stable through 2026, to say nothing of 2030. A History of Obsolete Optimizations Probably better as a really long blog series. Each chapter would be broken up into two parts: A deep dive into a brilliant, elegant, insightful historical optimization designed to work within the constraints of that era's computing technology What we started doing instead, once we had more compute/network/storage available. c.f. A Spellchecker Used to Be a Major Feat of Software Engineering. Bonus topics would be brilliance obsoleted by standardization (like what people did before git and json were universal), optimizations we do today that may not stand the test of time, and optimizations from the past that did. Sphinx Internals I need this. I've spent so much goddamn time digging around in Sphinx and docutils source code I'm gonna throw up. Systems Distributed Talk Today! Online premier's at noon central / 5 PM UTC, here! I'll be hanging out to answer questions and be awkward. You ever watch a recording of your own talk? It's real uncomfortable! In this case because it's a field on one of Volcano's supertypes. I guess schemas gotta follow LSP too ↩